Many data sets can be represented in the form of a graph, namely, as a collection of nodes connected together by edges. In certain cases, the graph data is multi-modal, multi-relational, and/or multivariate. The graph data is multi-modal when it includes multiple different types of nodes. The graph data is multi-relational when the nodes can be connected together using multiple different types of edges. And the graph data is multivariate when each node (and/or each edge) can be characterized by multiple attributes.
A user may wish to provide a visual rendition of the graph data to gain a better understanding of general patterns, trends and other features in the graph data. Traditionally, the user has performed this task by representing the graph data as a collection of points (representing the nodes) that are connected together by lines (representing the edges). This approach may be effective for small graphs, but it quickly becomes unduly complex and confusing for larger data sets. Various techniques have been proposed to manage the complexity of such visual representations. Yet there is room for considerable improvement in this field.